Andreas Savvides

Bio: Andreas Savvides is an academic researcher from Yale University. The author has contributed to research in topics: Wireless sensor network & Visual sensor network. The author has an hindex of 7, co-authored 9 publications receiving 308 citations.

The BehaviorScope framework for enabling ambient assisted living

Abstract: The in-house monitoring of elders using intelligent sensors is a very desirable service that has the potential of increasing autonomy and independence while minimizing the risks of living alone. Because of this promise, the efforts of building such systems have been spanning for decades, but there is still a lot of room for improvement. Driven by the recent technology advances in many of the required components, in this article, we present a scalable framework for detailed behavior interpretation. Our framework supports in-house monitoring of elders using an intelligent gateway and a set of cheap commercially available sensors, in addition to more advanced camera-based human localization sensors and a client for GPS-enabled mobile phones that provides monitoring when outdoors. In this article, we report our experiences and present our current progress in three main components: sensors, middleware, and behavior interpretation mechanisms spanning from simple programmable rule-based alerts to algorithms for extracting the temporal routines of individuals.

Extracting spatiotemporal human activity patterns in assisted living using a home sensor network

Abstract: This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is triggered by the monitored person’s motion over space and time. Using this stream of symbols, the problem of human activity modeling is formulated as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor network produces, and is solved using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. The developed algorithm examines the person’s data over these 30 days and automatically extracts the person’s daily pattern.

Extracting Spatiotemporal Human Activity Patterns in Assisted Living using a Home Sensor Network

Abstract: This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is triggered by the monitored person’s motion over space and time. Using this stream of symbols, we formulate the problem of human activity modeling as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor network produces and solve it using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. Our algorithm examines the person’s data over these 30 days and automatically extracts the person’s daily pattern.

Extracting spatiotemporal human activity patterns in assisted living using a home sensor network

Abstract: This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is triggered by the monitored person's motion over space and time. Using this stream of symbols, we formulate the problem of human activity modeling as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor network produces and solve it using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. Our algorithm examines the person's data over these 30 days and automatically extracts the person's daily pattern.

BehaviorScope: Real-Time Remote Human Monitoring Using Sensor Networks

Abstract: In this demonstration we present the BehaviorScope, a system for interpreting human activity patterns using a sensor network and its application to elder monitoring in assisted living. The BehaviorScope provides a runtime, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. Human activities are described in high-level scripts that are directly mapped to a hierarchy of probabilistic grammars that parse low-level sensor measurements into high-level distinguishable activities. Activities of interest are pre-programmed into a specification that is used by the system to interpret the incoming sensor data stream. The system interprets the activities to generate summaries and other triggered notifications that are propagated to stakeholders via email and cell-phone text messages.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

A Survey on Ambient-Assisted Living Tools for Older Adults

Abstract: In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly

Abstract: Wireless-sensor-network-based home monitoring system for elderly activity behavior involves functional assessment of daily activities. In this paper, we reported a mechanism for estimation of elderly well-being condition based on usage of house-hold appliances connected through various sensing units. We defined two new wellness functions to determine the status of the elderly on performing essential daily activities. The developed system for monitoring and evaluation of essential daily activities was tested at the homes of four different elderly persons living alone and the results are encouraging in determining wellness of the elderly.

Semantic trajectories: Mobility data computation and annotation

Abstract: With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this article lies in a semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.

A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Abstract: Human Behaviour Analysis (HBA) is more and more being of interest for computer vision and artificial intelligence researchers. Its main application areas, like Video Surveillance and Ambient-Assisted Living (AAL), have been in great demand in recent years. This paper provides a review on HBA for AAL and ageing in place purposes focusing specially on vision techniques. First, a clearly defined taxonomy is presented in order to classify the reviewed works, which are consequently presented following a bottom-up abstraction and complexity order. At the motion level, pose and gaze estimation as well as basic human movement recognition are covered. Next, the mainly used action and activity recognition approaches are presented with examples of recent research works. Increasing the degree of semantics and the time interval involved in the HBA, finally the behaviour level is reached. Furthermore, useful tools and datasets are analysed in order to provide help for initiating projects.